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Adapting recommender systems to the requirements of personal health record systems
Abstract In the future many people in industrializeIn the future many people in industrialized countries will manage their personal health data electronically in centralized, reliable and trusted repositories - so-called personal health record systems (PHR). At this stage PHR systems still fail to satisfy the individual medical information needs of their users. Personalized recommendations could solve this problem. A first approach of integrating recommender system (RS) methodology into personal health records - termed health recommender system (HRS) - is presented. By exploitation of existing semantic networks like Wikipedia a health graph data structure is obtained. The data kept within such a graph represent health related concepts and are used to compute semantic distances among pairs of such concepts. A ranking procedure based on the health graph is outlined which enables a match between entries of a PHR system and health information artifacts. This way a PHR user will obtain individualized health information he might be interested in.lth information he might be interested in.
Abstractsub In the future many people in industrializeIn the future many people in industrialized countries will manage their personal health data electronically in centralized, reliable and trusted repositories - so-called personal health record systems (PHR). At this stage PHR systems still fail to satisfy the individual medical information needs of their users. Personalized recommendations could solve this problem. A first approach of integrating recommender system (RS) methodology into personal health records - termed health recommender system (HRS) - is presented. By exploitation of existing semantic networks like Wikipedia a health graph data structure is obtained. The data kept within such a graph represent health related concepts and are used to compute semantic distances among pairs of such concepts. A ranking procedure based on the health graph is outlined which enables a match between entries of a PHR system and health information artifacts. This way a PHR user will obtain individualized health information he might be interested in.lth information he might be interested in.
Bibtextype inproceedings  +
Doi 10.1145/1882992.1883053  +
Has author Wiesner M. + , Pfeifer D. +
Has extra keyword Information need + , Knowledge mining + , Recommender system + , Relevance computation + , Wikipedia + , Data mining + , Data structures + , Graph theory + , Health care + , Information science + , Semantics + , Health +
Has keyword Graph theory + , Health care + , Information needs + , Knowledge mining + , Recommender system + , Relevance computation + , Wikipedia +
Isbn 9781450300308  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 410–414  +
Published in IHI'10 - Proceedings of the 1st ACM International Health Informatics Symposium +
Title Adapting recommender systems to the requirements of personal health record systems +
Type conference paper  +
Year 2010 +
Creation dateThis property is a special property in this wiki. 6 November 2014 18:35:25  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 6 November 2014 18:35:25  +
DateThis property is a special property in this wiki. 2010  +
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